Gregory Kiar

BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.

To the Cloud! A Grassroots Proposal to Accelerate Brain Science Discovery

The revolution in neuroscientific data acquisition is creating an analysis challenge. We propose leveraging cloud-computing technologies to enable large-scale neurodata storing, exploring, analyzing, and modeling. This utility will empower scientists globally to generate and test theories of brain function and dysfunction.

Science in the cloud (SIC): A use case in MRI connectomics

Abstract Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught of data, we can allow the focal point to shift from data collection to data analysis. Unfortunately, lack of standardized sharing mechanisms and practices often make reproducing or extending scientific results very difficult. With the creation of data organization structures and tools that drastically improve code portability, we now have the opportunity to design such a framework for communicating extensible scientific discoveries. Our proposed solution leverages these existing technologies and standards, and provides an accessible and extensible model for reproducible research, called ‘science in the cloud’ (SIC). Exploiting scientific containers, cloud computing, and cloud data services, we show the capability to compute in the cloud and run a web service that enables intimate interaction with the tools and data presented. We hope this model will inspire the community to produce reproducible and, importantly, extensible results that will enable us to collectively accelerate the rate at which scientific breakthroughs are discovered, replicated, and extended.

A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability

Modern scientific discovery depends on collecting large heterogeneous datasets with many sources of variability, and applying domain-specific pipelines from which one can draw insight or clinical utility. For example, macroscale connectomics studies require complex pipelines to process raw functional or diffusion data and estimate connectomes. Individual studies tend to customize pipelines to their needs, raising concerns about their reproducibility, which add to a longer list of factors that may differ across studies and result in failures to replicate (including sampling, experimental design, and data acquisition protocols). Mitigating these issues requires multi-study datasets and the development of pipelines that can be applied across them. We developed NeuroData’s MRI to Graphs (NDMG) pipeline using several functional and diffusion studies, including the Consortium for Reliability and Reproducability, to estimate connectomes. Without any manual intervention or parameter tuning, NDMG ran on 25 different studies (≈6,000 scans) from 19 sites, with each scan resulting in a biologically plausible connectome (as assessed by multiple quality assurance metrics at each processing stage). For each study, the connectomes from NDMG are more similar within than across individuals, indicating that NDMG is preserving biological variability. Moreover, the connectomes exhibit near perfect consistency for certain connectional properties across every scan, individual, study, site and modality; these include stronger ipsilateral than contralateral connections and stronger homotopic than heterotopic connections. Yet, the magnitude of the differences varied across individuals and studies—much more so when pooling data across sites, even after controlling for study, site, and basic demographic variables (i.e., age, sex, and ethnicity). This indicates that other experimental variables (possibly those not measured or reported) are contributing to this variability, which if not accounted for can limit the value of aggregate datasets, as well as expectations regarding the accuracy of findings and likelihood of replication. We therefore provide a set of principles to guide the development of pipelines capable of pooling data across studies while maintaining biological variability and minimizing measurement error. This open science approach provides us with an opportunity to understand and eventually mitigate spurious results for both past and future studies.The connectivity of the human brain is fundamental to understanding the principles of cognitive function, and the mechanisms by which it can go awry. To that extent, tools for estimating human brain networks are required for single participant, group level, and cross-study analyses. We have developed an open-source, cloud-enabled, turn-key pipeline that operates on (groups of) raw diffusion and structure magnetic resonance imaging data, estimating brain networks (connectomes) across 24 different spatial scales, with quality assurance visualizations at each stage of processing. Running a harmonized analysis on 10 different datasets comprising 2,295 subjects and 2,861 scans reveals that the connectomes across datasets are similar on coarse scales, but quantitatively different on fine scales. Our framework therefore illustrates that while general principles of human brain organization may be preserved across experiments, obtaining reliable p-values and clinical biomarkers from connectomics will require further harmonization efforts.

HETEROGENEOUS TAU-PET SIGNAL IN THE HIPPOCAMPUS HELPS RESOLVE DISCREPANCIES BETWEEN IMAGING AND PATHOLOGY

Background:Current methods of amyloid-PET interpretation fail to identify early phases of amyloid deposition. Grothe et al. recently used Florbetapir-PET data from the ADNI cohort to develop a hierarchical stage model of PET-evidenced amyloid deposition (Fig. 1) resembling the estimation reported in neuropathologic studies. This in vivo hierarchical stage allowed classifying over 95% of the individual amyloid deposition profiles into one of four amyloid stages (Grothe et al., Neurology, 2017). Here we evaluated the replicability of Grothe in-vivo amyloid staging in an independent cohort of the INSIGHT-preAD study. We further explored potential benefits of this in-vivo amyloid staging approach for predicting incipient cognitive decline in this preclinical cohort.Methods:The monocentric INSIGHT-preAD cohort includes florbetapir-PET data from 318 cognitively intact older individuals with subjective memory complaints (SMC). All individuals underwent extensive neuropsychological testing at baseline, and a subset (N1⁄4265) was repeatedly tested at 6-months intervals for 2 years of follow-up. After initial pre-processing of the Florbetapir-PET data, we projected it into the previously proposed four-stage model of amyloid progression. Associations between in-vivo amyloid stage and cognitive decline were assessed cross-sectionally using ANCOVA, as well as longitudinally using a latent class growth modeling (LCGM) approach. Results obtained using the regional staging model were compared to the conventional dichotomization based on a global signal cutoff (SUVRcereb 1⁄4 1.1). Results: 38.7% of individuals were identified as having detectable amyloid load, and only 6 (4.9%) of these violated the proposed regional hierarchy. Compared to conven-